The responses to the questions did not save:
What it does It allows the user to input an image of a car from an angle and gives them a verdict on weather or not it was AI-generated or a real photo of a car. It also gives a natural language explanation of the decision based off of the verdict and confidence score.
How we built it It is built entirely in python and utilizes gradio for the frontend/UI. I trained the ResNet18 model from the pytorch on my datasets and then deployed it on the Tiber cloud. After the user uploads an image, the agent has a pipeline that processes the image and runs it through the model. It first removes the background and then converts it into a tensor. After the model prediction, we get a confidence score and verdict(targe/non-target).
Accomplishments that we're proud of I am most proud of the model training process as it required me to generate my own dataset and process it thoroughly to produce an accurate model. I am also proud of the natural language implementation as I believe it gave it a bit of a unique twist.
What we learned: I'd never used hugging face prior to this hackathon and didn't realize how useful it was. I'm glad that I got to learn about it and will definitely use it in the future.
What's next for AutoAuth Given more time, I would have liked to train separate models for the front, back and side views of the cars. Additionally I think some sort of heatmap implementation with Grad Cam would be useful as it would aid in decision explainability.
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